A Comparison Study on Severe Accident Risks Between PWR and PHWR Plants
Bibliographic record
Abstract
The health effects resulting from severe accidents of typical 1,000MWe KSNP(Korea Standard Nuclear Plant) PWR and typical 600MWe CANDU(CANada Deuterium Uranium) plants were estimated and compared. The population distribution of the site extending to 80km for both site were considered. The releaese fraction for various source term categories(STC) and core inventories were used in the estimation of the health effects risks by using the MACCS2(MELCOR Accident Consequence Code System2) code. Individuals are assumed to evacuate beyond 16km from the site. The health effects considered in this comparative study are early and cancer fatality risk, and the results are presented as CCDF(Complementary Cumulative Distribution Function) curves considering the occurrence probability of each STC's. According to the results, the early and cancer fatality risks of PHWR plants we lower than those of PWR plants. This is attributed the fact that the amount of radioactive mateials that released to the atmosphere resulting from the postulated severe accidents of PHWR plants are smaller than that of PWR plants. And, the dominating initiating event of STC that shows maximum early and cancer fatality risk is SGTR(Steam Generator Tube Rupture) for both plants. Therefore, the appropriated actions must be taken to reduce the occurrence probability and the amounts of radioactive materials released to the environment in order to protect the public for both PWR and PHWR plants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".